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  1. Silicon-based spin qubits represent a promising technology for scalable quantum computing. However, the complex nature of this field, which requires a deep understanding of quantum mechanics, materials science, and nanoelectronics, poses a significant challenge in making it accessible to future engineers and scientists. Spin Quantum Gate Lab, a spin qubit simulation tool, is proposed in this paper to address this obstacle. This tool is designed to introduce key concepts of spin qubit to undergraduate students, enabling the simulation of single-qubit rotational gates and two-qubit controlled-phase gates. By providing hands-on experience with quantum gate operations, it effectively links theoretical quantum concepts to practical experience, fostering a deeper understanding of silicon-based quantum computing. 
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    Free, publicly-accessible full text available September 15, 2025
  2. Abstract: There is a consensus to introduce data science to secondary schools. However, data science is interdisciplinary in nature and not easy to teach in K−12 settings. We proposed a new approach to integrate mathematics, statistics, and programming—the foundations of data science—for high school students based on set theory and logic. We developed an 8-week data science foundation course and implemented it in a public high school. We conducted semistructured group interviews to collect students’ feedback on the course and the new approach. Students thought the approach could well connect the topics and helped them learn the interdisciplinary content. 
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    Free, publicly-accessible full text available June 10, 2025
  3. Free, publicly-accessible full text available June 23, 2025
  4. Students of all socioeconomic backgrounds love music and express their identity through music. There are strong historical connections between music and computing, and computer-based music has a heavy presence in contemporary popular culture. Thus, programming electronic music can provide the type of authentic learning experience that fosters participation in computer science (CS) by minoritized students. Although important efforts have been made in that direction, they have not reached young children in mainstream public classrooms, particularly in schools serving children from low-income and marginalized backgrounds. Developing a computational tool and educational program that reaches this key demographic holds the potential to greatly increase CS knowledge and participation in the future workforce. For this, our team has created M-flow, a flow-based music programming platform that seeks to be engaging for children from the outset, and that makes it extremely easy for non-specialized teachers to learn and implement CS activities in the classroom. 
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  5. Participating in online communities has significant benefits to students learning in terms of students’ motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support. 
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